EXIN BCS AI Essentials study plan

Define artificial intelligence and distinguish it from general automation, using at least three contrasting examples of each

Sequence the major historical milestones of AI from the 1950s Dartmouth workshop era through the AI winters to modern deep learning, and state what triggered each downturn

Classify the main schools of thought (symbolic versus connectionist, strong versus weak AI) and give one representative claim for each

Explain the Turing test, its purpose and its principal criticisms, in your own words

Compare human and machine capabilities across perception, reasoning, learning and creativity, naming at least two tasks where each side currently leads

Explain the concepts of narrow, general and superintelligence and place current systems correctly on that spectrum

Describe how knowledge representation and search underpin classical AI problem solving, using a simple worked example

Identify three cognitive biases in human decision making and contrast them with typical machine failure modes

Catalogue at least eight real-world AI application areas (for example healthcare diagnostics, fraud detection, recommendation, transport) and state the benefit each delivers

Distinguish the categories of AI risk: bias and fairness, privacy, safety, transparency, accountability and labour-market impact

Analyse a documented case of algorithmic bias and explain the mechanism that produced the unfair outcome

Explain the difference between explainable and black-box models and why the distinction matters in regulated sectors

Consolidate domains 1 and 2 by answering mixed practice questions under timed conditions

Diagnose your two weakest sub-topics from practice results and rewrite your own summary notes for each

Debate one AI ethics dilemma (for example autonomous vehicles or predictive policing) and argue both sides in writing

Map each domain 1 and 2 syllabus item to a specific note or example you can recall without prompts

Distinguish supervised, unsupervised and reinforcement learning, and match each to an appropriate business problem

Explain the machine learning pipeline from data collection and feature selection through training, validation and deployment

Describe overfitting and underfitting, and explain the roles of training, validation and test sets in detecting them

Interpret the confusion matrix and define accuracy, precision, recall and F1 score with a worked numeric example

Compare at least four algorithm families (decision trees, k-nearest neighbours, neural networks, clustering) by use case, interpretability and data requirements

Explain the difference between classification and regression and select the right type for three given scenarios

Describe how data quality, labelling and sampling choices affect model performance and fairness

Outline how a neural network learns, covering layers, weights and gradient-based adjustment, without mathematical derivation

Explain the concept of human-machine collaboration and give three examples where combined performance exceeds either alone

Assess the future impact of AI on work, describing tasks likely to be automated versus augmented and the reskilling implications

Summarise the direction of AI governance and regulation, including the purpose of risk-based regulatory approaches

Articulate the limits of current AI, including lack of common-sense reasoning and dependence on training data

Complete two full-length timed mock exams covering all four domains and record scores per domain

Review every incorrect answer and write the correct reasoning in one sentence each

Recite the key definitions, frameworks and risk categories from memory using flashcards

Finalise a one-page summary sheet you can review in the 48 hours before the exam